class ExecutionGraph(DAG, PickleInterface):
"""
Datastructure that tracks, executes, and reports on study execution.
The ExecutionGraph is used to manage, monitor, and interact with tasks and
the scheduler. This class searches its graph for tasks that are ready to
run, marks tasks as complete, and schedules ready tasks.
The Execution class is where functionality for checking task status, logic
for managing and automatically directing and manipulating the workflow
should go. Essentially, if logic is needed to automatically manipulate the
workflow in some fashion or additional monitoring is needed, this class is
where that would go.
"""
def __init__(self, submission_attempts=1, submission_throttle=0,
use_tmp=False, dry_run=False):
"""
Initialize a new instance of an ExecutionGraph.
:param submission_attempts: Number of attempted submissions before
marking a step as failed.
:param submission_throttle: Maximum number of scheduled in progress
submissions.
:param use_tmp: A Boolean value that when set to 'True' designates
that ExecutionGraph should use temporary files for output.
"""
super(ExecutionGraph, self).__init__()
# Member variables for execution.
self._adapter = None
self._description = OrderedDict()
# Generate tempdir (if specfied)
if use_tmp:
self._tmp_dir = tempfile.mkdtemp()
else:
self._tmp_dir = ""
# Sets to track progress.
self.completed_steps = set([SOURCE])
self.in_progress = set()
self.failed_steps = set()
self.cancelled_steps = set()
self.ready_steps = deque()
self.is_canceled = False
self._status_order = 'bfs' # Set status order type
self._status_subtree = None # Cache bfs_subtree for status writing
# Values for management of the DAG. Things like submission attempts,
# throttling, etc. should be listed here.
self._submission_attempts = submission_attempts
self._submission_throttle = submission_throttle
self.dry_run = dry_run
# A map that tracks the dependencies of a step.
# NOTE: I don't know how performant the Python dict structure is, but
# we'll use it for now. I think this may want to be changed to an AVL
# tree or something of that nature to guarantee worst case performance.
self._dependencies = {}
LOGGER.info(
"\n------------------------------------------\n"
"Submission attempts = %d\n"
"Submission throttle limit = %d\n"
"Use temporary directory = %s\n"
"Tmp Dir = %s\n"
"------------------------------------------",
submission_attempts, submission_throttle, use_tmp, self._tmp_dir
)
# Error check that the submission values are valid.
if self._submission_attempts < 1:
_msg = "Submission attempts should always be greater than 0. " \
"Received a value of {}.".format(self._submission_attempts)
LOGGER.error(_msg)
raise ValueError(_msg)
if self._submission_throttle < 0:
_msg = "Throttling should be 0 for unthrottled or a positive " \
"integer for the number of allowed inflight jobs. " \
"Received a value of {}.".format(self._submission_throttle)
LOGGER.error(_msg)
raise ValueError(_msg)
def _check_tmp_dir(self):
"""Check and recreate the tempdir should it have been erased."""
# If we've specified a tmp dir and the previous tmp dir doesn't exist
# recreate it.
if self._tmp_dir and not os.path.exists(self._tmp_dir):
self._tmp_dir = tempfile.mkdtemp()
def add_step(self, name, step, workspace, restart_limit, params=None):
"""
Add a StepRecord to the ExecutionGraph.
:param name: Name of the step to be added.
:param step: StudyStep instance to be recorded.
:param workspace: Directory path for the step's working directory.
:param restart_limit: Upper limit on the number of restart attempts.
:param params: Iterable of tuples of step parameter names, values
"""
data = {
"step": step,
"state": State.INITIALIZED,
"workspace": workspace,
"restart_limit": restart_limit,
}
record = _StepRecord(**data)
if params:
record.add_params(params)
self._dependencies[name] = set()
super(ExecutionGraph, self).add_node(name, record)
def add_connection(self, parent, step):
"""
Add a connection between two steps in the ExecutionGraph.
:param parent: The parent step that is required to execute 'step'
:param step: The dependent step that relies on parent.
"""
self.add_edge(parent, step)
self._dependencies[step].add(parent)
def set_adapter(self, adapter):
"""
Set the adapter used to interface for scheduling tasks.
:param adapter: Adapter name to be used when launching the graph.
"""
if not adapter:
# If we have no adapter specified, assume sequential execution.
self._adapter = None
return
if not isinstance(adapter, dict):
msg = "Adapter settings must be contained in a dictionary."
LOGGER.error(msg)
raise TypeError(msg)
# Check to see that the adapter type is something the
if adapter["type"] not in ScriptAdapterFactory.get_valid_adapters():
msg = "'{}' adapter must be specfied in ScriptAdapterFactory." \
.format(adapter)
LOGGER.error(msg)
raise TypeError(msg)
self._adapter = adapter
def add_description(self, name, description, **kwargs):
"""
Add a study description to the ExecutionGraph instance.
:param name: Name of the study.
:param description: Description of the study.
"""
self._description["name"] = name
self._description["description"] = description
self._description.update(kwargs)
@property
def name(self):
"""
Return the name for the study in the ExecutionGraph instance.
:returns: A string of the name of the study.
"""
return self._description["name"]
@name.setter
def name(self, value):
"""
Set the name for the study in the ExecutionGraph instance.
:param name: A string of the name for the study.
"""
self._description["name"] = value
@property
def description(self):
"""
Return the description for the study in the ExecutionGraph instance.
:returns: A string of the description for the study.
"""
return self._description["description"]
@description.setter
def description(self, value):
"""
Set the description for the study in the ExecutionGraph instance.
:param value: A string of the description for the study.
"""
self._description["description"] = value
def log_description(self):
"""Log the description of the ExecutionGraph."""
desc = ["{}: {}".format(key, value)
for key, value in self._description.items()]
desc = "\n".join(desc)
LOGGER.info(
"\n==================================================\n"
"%s\n"
"==================================================\n",
desc
)
def generate_scripts(self):
"""
Generate the scripts for all steps in the ExecutionGraph.
The generate_scripts method scans the ExecutionGraph instance and uses
the stored adapter to write executable scripts for either local or
scheduled execution. If a restart command is specified, a restart
script will be generated for that record.
"""
# An adapter must be specified
if not self._adapter:
msg = "Adapter not found. Specify a ScriptAdapter using " \
"set_adapter."
LOGGER.error(msg)
raise ValueError(msg)
# Set up the adapter.
LOGGER.info("Generating scripts...")
adapter = ScriptAdapterFactory.get_adapter(self._adapter["type"])
adapter = adapter(**self._adapter)
self._check_tmp_dir()
for key, record in self.values.items():
if key == SOURCE:
continue
# Record generates its own script.
record.setup_workspace()
record.generate_script(adapter, self._tmp_dir)
def _execute_record(self, record, adapter, restart=False):
"""
Execute a StepRecord.
:param record: The StepRecord to be executed.
:param adapter: An instance of the adapter to be used for cluster
submission.
:param restart: True if the record needs restarting, False otherwise.
"""
# Logging for debugging.
LOGGER.info("Calling execute for StepRecord '%s'", record.name)
num_restarts = 0 # Times this step has temporally restarted.
retcode = None # Execution return code.
# While our submission needs to be submitted, keep trying:
# 1. If the JobStatus is not OK.
# 2. num_restarts is less than self._submission_attempts
self._check_tmp_dir()
# Only set up the workspace the initial iteration.
if not restart:
LOGGER.debug("Setting up workspace for '%s' at %s",
record.name, str(datetime.now()))
# Generate the script for execution on the fly.
record.setup_workspace() # Generate the workspace.
record.generate_script(adapter, self._tmp_dir)
if self.dry_run:
record.mark_end(State.DRYRUN)
self.completed_steps.add(record.name)
return
while retcode != SubmissionCode.OK and \
num_restarts < self._submission_attempts:
LOGGER.info("Attempting submission of '%s' (attempt %d of %d)...",
record.name, num_restarts + 1,
self._submission_attempts)
# We're not restarting -- submit as usual.
if not restart:
LOGGER.debug("Calling 'execute' on '%s' at %s",
record.name, str(datetime.now()))
retcode = record.execute(adapter)
# Otherwise, it's a restart.
else:
# If the restart is specified, use the record restart script.
LOGGER.debug("Calling 'restart' on '%s' at %s",
record.name, str(datetime.now()))
# Generate the script for execution on the fly.
record.generate_script(adapter, self._tmp_dir)
retcode = record.restart(adapter)
# Increment the number of restarts we've attempted.
LOGGER.debug("Completed submission attempt %d", num_restarts)
num_restarts += 1
sleep((random.random() + 1) * num_restarts)
if retcode == SubmissionCode.OK:
self.in_progress.add(record.name)
if record.is_local_step:
LOGGER.info("Local step %s executed with status OK. Complete.",
record.name)
record.mark_end(State.FINISHED)
self.completed_steps.add(record.name)
self.in_progress.remove(record.name)
else:
# Find the subtree, because anything dependent on this step now
# failed.
LOGGER.warning("'%s' failed to submit properly. "
"Step failed.", record.name)
path, parent = self.bfs_subtree(record.name)
for node in path:
self.failed_steps.add(node)
self.values[node].mark_end(State.FAILED)
# After execution state debug logging.
LOGGER.debug("After execution of '%s' -- New state is %s.",
record.name, record.status)
@property
def status_subtree(self):
"""Cache the status ordering to improve scaling"""
if not self._status_subtree:
if self._status_order == 'bfs':
subtree, _ = self.bfs_subtree("_source")
elif self._status_order == 'dfs':
subtree, _ = self.dfs_subtree("_source", par="_source")
self._status_subtree = [key for key in subtree
if key != '_source']
return self._status_subtree
def write_status(self, path):
"""Write the status of the DAG to a CSV file."""
header = "Step Name,Job ID,Workspace,State,Run Time,Elapsed Time," \
"Start Time,Submit Time,End Time,Number Restarts,Params"
status = [header]
for key in self.status_subtree:
value = self.values[key]
jobid_str = "--"
if value.jobid:
jobid_str = str(value.jobid[-1])
# Include step root in workspace when parameterized
if list(value.params.items()):
ws = os.path.join(
* os.path.normpath(
value.workspace.value).split(os.sep)[-2:]
)
else:
ws = os.path.split(value.workspace.value)[1]
_ = [
value.name, jobid_str,
ws,
str(value.status.name), value.run_time, value.elapsed_time,
value.time_start, value.time_submitted, value.time_end,
str(value.restarts),
";".join(["{}:{}".format(param, value)
for param, value in value.params.items()])
]
_ = ",".join(_)
status.append(_)
stat_path = os.path.join(path, "status.csv")
lock_path = os.path.join(path, ".status.lock")
lock = FileLock(lock_path)
try:
with lock.acquire(timeout=10):
with open(stat_path, "w+") as stat_file:
stat_file.write("\n".join(status))
except Timeout:
pass
def _check_study_completion(self):
# We cancelled, return True marking study as complete.
if self.is_canceled:
LOGGER.info("Cancelled -- completing study.")
return StudyStatus.CANCELLED
# check for completion of all steps
resolved_set = \
self.completed_steps | self.failed_steps | self.cancelled_steps
if not set(self.values.keys()) - resolved_set:
# some steps were cancelled and is_canceled wasn't set
if len(self.cancelled_steps) > 0:
logging.info("'%s' was cancelled. Returning.", self.name)
return StudyStatus.CANCELLED
# some steps were failures indicating failure
if len(self.failed_steps) > 0:
logging.info("'%s' is complete with failures. Returning.",
self.name)
return StudyStatus.FAILURE
# everything completed were are done
logging.info("'%s' is complete. Returning.", self.name)
return StudyStatus.FINISHED
return StudyStatus.RUNNING
def execute_ready_steps(self):
"""
Execute any steps whose dependencies are satisfied.
The 'execute_ready_steps' method is the core of how the ExecutionGraph
manages execution. This method does the following:
* Checks the status of existing jobs that are executing and updates
the state if changed.
* Finds steps that are initialized and determines what can be run
based on satisfied dependencies and executes steps whose
dependencies are met.
:returns: True if the study has completed, False otherwise.
"""
# TODO: We may want to move this to a singleton somewhere
# so we can guarantee that all steps use the same adapter.
adapter = ScriptAdapterFactory.get_adapter(self._adapter["type"])
adapter = adapter(**self._adapter)
if not self.dry_run:
LOGGER.debug("Checking status check...")
retcode, job_status = self.check_study_status()
else:
LOGGER.debug("DRYRUN: Skipping status check...")
retcode = JobStatusCode.OK
job_status = {}
LOGGER.debug("Checked status (retcode %s)-- %s", retcode, job_status)
# For now, if we can't check the status something is wrong.
# Don't modify the DAG.
if retcode == JobStatusCode.ERROR:
msg = "Job status check failed -- Aborting."
LOGGER.error(msg)
raise RuntimeError(msg)
elif retcode == JobStatusCode.OK:
# For the status of each currently in progress job, check its
# state.
cleanup_steps = set() # Steps that are in progress showing failed.
for name, status in job_status.items():
LOGGER.debug("Checking job '%s' with status %s.", name, status)
record = self.values[name]
if status == State.FINISHED:
# Mark the step complete and notate its end time.
record.mark_end(State.FINISHED)
LOGGER.info("Step '%s' marked as finished. Adding to "
"complete set.", name)
self.completed_steps.add(name)
self.in_progress.remove(name)
elif status == State.RUNNING:
# When detect that a step is running, mark it.
LOGGER.info("Step '%s' found to be running.", record.name)
record.mark_running()
elif status == State.TIMEDOUT:
# Execute the restart script.
# If a restart script doesn't exist, re-run the command.
# If we're under the restart limit, attempt a restart.
if record.can_restart:
if record.mark_restart():
LOGGER.info(
"Step '%s' timed out. Restarting (%s of %s).",
name, record.restarts, record.restart_limit
)
self._execute_record(record, adapter, restart=True)
else:
LOGGER.info("'%s' has been restarted %s of %s "
"times. Marking step and all "
"descendents as failed.",
name,
record.restarts,
record.restart_limit)
self.in_progress.remove(name)
cleanup_steps.update(self.bfs_subtree(name)[0])
# Otherwise, we can't restart so mark the step timed out.
else:
LOGGER.info("'%s' timed out, but cannot be restarted."
" Marked as TIMEDOUT.", name)
# Mark that the step ended due to TIMEOUT.
record.mark_end(State.TIMEDOUT)
# Remove from in progress since it no longer is.
self.in_progress.remove(name)
# Add the subtree to the clean up steps
cleanup_steps.update(self.bfs_subtree(name)[0])
# Remove the current step, clean up is used to mark
# steps definitively as failed.
cleanup_steps.remove(name)
# Add the current step to failed.
self.failed_steps.add(name)
elif status == State.HWFAILURE:
# TODO: Need to make sure that we do this a finite number
# of times.
# Resubmit the cmd.
LOGGER.warning("Hardware failure detected. Attempting to "
"resubmit step '%s'.", name)
# We can just let the logic below handle submission with
# everything else.
self.ready_steps.append(name)
elif status == State.FAILED:
LOGGER.warning(
"Job failure reported. Aborting %s -- flagging all "
"dependent jobs as failed.",
name
)
self.in_progress.remove(name)
record.mark_end(State.FAILED)
cleanup_steps.update(self.bfs_subtree(name)[0])
elif status == State.UNKNOWN:
record.mark_end(State.UNKNOWN)
LOGGER.info(
"Step '%s' found in UNKNOWN state. Step was found "
"in '%s' state previously, marking as UNKNOWN. "
"Adding to failed steps.",
name, record.status)
cleanup_steps.update(self.bfs_subtree(name)[0])
self.in_progress.remove(name)
elif status == State.CANCELLED:
LOGGER.info("Step '%s' was cancelled.", name)
self.in_progress.remove(name)
record.mark_end(State.CANCELLED)
# Let's handle all the failed steps in one go.
for node in cleanup_steps:
self.failed_steps.add(node)
self.values[node].mark_end(State.FAILED)
# Now that we've checked the statuses of existing jobs we need to make
# sure dependencies haven't been met.
for key in self.values.keys():
# We MUST dereference from the key. If we use values.items(), a
# generator gets produced which will give us a COPY of a record and
# not the actual record.
record = self.values[key]
# A completed step by definition has had its dependencies met.
# Skip it.
if key in self.completed_steps:
LOGGER.debug("'%s' in completed set, skipping.", key)
continue
LOGGER.debug("Checking %s -- %s", key, record.jobid)
# If the record is only INITIALIZED, we have encountered a step
# that needs consideration.
if record.status == State.INITIALIZED:
LOGGER.debug("'%s' found to be initialized. Checking "
"dependencies. ", key)
LOGGER.debug(
"Unfulfilled dependencies: %s",
self._dependencies[key])
s_completed = filter(
lambda x: x in self.completed_steps,
self._dependencies[key])
self._dependencies[key] = \
self._dependencies[key] - set(s_completed)
LOGGER.debug(
"Completed dependencies: %s\n"
"Remaining dependencies: %s",
s_completed, self._dependencies[key])
# If the gating dependencies set is empty, we can execute.
if not self._dependencies[key]:
if key not in self.ready_steps:
LOGGER.debug("All dependencies completed. Staging.")
self.ready_steps.append(key)
else:
LOGGER.debug("Already staged. Passing.")
continue
# We now have a collection of ready steps. Execute.
# If we don't have a submission limit, go ahead and submit all.
if self._submission_throttle == 0:
LOGGER.info("Launching all ready steps...")
_available = len(self.ready_steps)
# Else, we have a limit -- adhere to it.
else:
# Compute the number of available slots we have for execution.
_available = self._submission_throttle - len(self.in_progress)
# Available slots should never be negative, but on the off chance
# we are in a slot deficit, then we will just say none are free.
_available = max(0, _available)
# Now, we need to take the min of the length of the queue and the
# computed number of slots. We could have free slots, but have less
# in the queue.
_available = min(_available, len(self.ready_steps))
LOGGER.info("Found %d available slots...", _available)
for i in range(0, _available):
# Pop the record and execute using the helper method.
_record = self.values[self.ready_steps.popleft()]
# If we get to this point and we've cancelled, cancel the record.
if self.is_canceled:
LOGGER.info("Cancelling '%s' -- continuing.", _record.name)
_record.mark_end(State.CANCELLED)
self.cancelled_steps.add(_record.name)
continue
LOGGER.debug("Launching job %d -- %s", i, _record.name)
self._execute_record(_record, adapter)
# check the status of the study upon finishing this round of execution
completion_status = self._check_study_completion()
return completion_status
def check_study_status(self):
"""
Check the status of currently executing steps in the graph.
This method is used to check the status of all currently in progress
steps in the ExecutionGraph. Each ExecutionGraph stores the adapter
used to generate and execute its scripts.
"""
# Set up the job list and the map to get back to step names.
joblist = []
jobmap = {}
for step in self.in_progress:
jobid = self.values[step].jobid[-1]
joblist.append(jobid)
jobmap[jobid] = step
# Grab the adapter from the ScriptAdapterFactory.
adapter = ScriptAdapterFactory.get_adapter(self._adapter["type"])
adapter = adapter(**self._adapter)
# Use the adapter to grab the job statuses.
retcode, job_status = adapter.check_jobs(joblist)
# Map the job identifiers back to step names.
step_status = {jobmap[jobid]: status
for jobid, status in job_status.items()}
# Based on return code, log something different.
if retcode == JobStatusCode.OK:
LOGGER.info("Jobs found for user '%s'.", getpass.getuser())
return retcode, step_status
elif retcode == JobStatusCode.NOJOBS:
LOGGER.info("No jobs found.")
return retcode, step_status
else:
msg = "Unknown Error (Code = {})".format(retcode)
LOGGER.error(msg)
return retcode, step_status
def cancel_study(self):
"""Cancel the study."""
joblist = []
for step in self.in_progress:
jobid = self.values[step].jobid[-1]
joblist.append(jobid)
# Grab the adapter from the ScriptAdapterFactory.
adapter = ScriptAdapterFactory.get_adapter(self._adapter["type"])
adapter = adapter(**self._adapter)
# cancel our jobs
crecord = adapter.cancel_jobs(joblist)
self.is_canceled = True
if crecord.cancel_status == CancelCode.OK:
LOGGER.info("Successfully requested to cancel all jobs.")
elif crecord.cancel_status == CancelCode.ERROR:
LOGGER.error(
"Failed to cancel jobs. (Code = %s)", crecord.return_code)
else:
LOGGER.error("Unknown Error (Code = %s)", crecord.return_code)
return crecord.cancel_status
def cleanup(self):
"""Clean up output produced by the ExecutionGraph."""
if self._tmp_dir:
shutil.rmtree(self._tmp_dir, ignore_errors=True)